Short-term wind speed prediction is essential for economical wind power utilization. The real-world wind speed data is typically intermittent and fluctuating, presenting great challenges to existing shallow models. In this paper, we present a novel deep hybrid model for multi-step wind speed prediction, namely LR-FFT-RP-MLP/LSTM (Linear Fast Fourier Transformation Rank Pooling Multiple-Layer Perception/Long Short-Term Memory). Our hybrid model processes the local and global input features simultaneously. We leverage Rank Pooling (RP) for the local feature extraction to capture the temporal structure while maintaining the temporal order. Besides, to understand the wind periodic patterns, we exploit Fast Fourier Transformation (FFT) to extract global features and relevant frequency components in the wind speed data. The resulting local and global features are respectively integrated with the original data and are fed into an MLP/LSTM layer for the initial wind speed predictions. Finally, we leverage a linear regression layer to collaborate these initial predictions to produce the final wind speed prediction. The proposed hybrid model is evaluated using real wind speed data collected from 2010 to 2020, demonstrating superior forecasting capabilities when compared to state-of-the-art single and hybrid models. Overall, this study presents a promising approach for improving the accuracy of wind speed forecasting.

翻译：短期风速预测对于实现经济高效的风能利用至关重要。实际风速数据通常具有间歇性和波动性，给现有浅层模型带来了巨大挑战。本文提出了一种新颖的深度混合模型用于多步风速预测，即LR-FFT-RP-MLP/LSTM（线性快速傅里叶变换排序池化多层感知机/长短期记忆网络）。该混合模型可同时处理局部和全局输入特征。我们利用排序池化（RP）提取局部特征，在保持时间顺序的前提下捕获时序结构。此外，为理解风的周期模式，我们采用快速傅里叶变换（FFT）提取全局特征及风速数据中相关的频率分量。所提取的局部与全局特征分别与原始数据融合后，输入至MLP/LSTM层进行初始风速预测。最后，通过线性回归层融合这些初始预测结果，生成最终的风速预测。基于2010年至2020年实测风速数据的评估表明，该混合模型相比现有最先进的单一模型和混合模型展现出更优的预测能力。总体而言，本研究为提升风速预测精度提供了一种有前景的方法。